These tools will no longer be maintained as of December 31, 2024. Archived website can be found here. PubMed4Hh GitHub repository can be found here. Contact NLM Customer Service if you have questions.
210 related articles for article (PubMed ID: 33946026)
41. Interpretability analysis for thermal sensation machine learning models: An exploration based on the SHAP approach. Yang Y; Yuan Y; Han Z; Liu G Indoor Air; 2022 Feb; 32(2):e12984. PubMed ID: 35048421 [TBL] [Abstract][Full Text] [Related]
42. Ecoregions, climate, topography, physicochemical, or a combination of all: Which criteria are the best to define river types based on abiotic variables and macroinvertebrates in neotropical rivers? Pero EJI; Georgieff SM; Gultemirian ML; Romero F; Hankel GE; Domínguez E Sci Total Environ; 2020 Oct; 738():140303. PubMed ID: 32806352 [TBL] [Abstract][Full Text] [Related]
43. Impact of hydromorphology and spatial scale on macroinvertebrate assemblage composition in streams. Verdonschot PF Integr Environ Assess Manag; 2009 Jan; 5(1):97-109. PubMed ID: 19431295 [TBL] [Abstract][Full Text] [Related]
44. [Application of species distribution models in the prediction of marine potential habitat: A review]. Yang XL; Yang CJ; Hu CY; Zhang XM Ying Yong Sheng Tai Xue Bao; 2017 Jun; 28(6):2063-2072. PubMed ID: 29745172 [TBL] [Abstract][Full Text] [Related]
45. Interpretable and explainable AI (XAI) model for spatial drought prediction. Dikshit A; Pradhan B Sci Total Environ; 2021 Dec; 801():149797. PubMed ID: 34467917 [TBL] [Abstract][Full Text] [Related]
47. Patterns of macroinvertebrate assemblages in a long-term watershed-scale study to address the effects of pulp and paper mill discharges in four US receiving streams. Flinders CA; Minshall GW; Ragsdale RL; Hall TJ Integr Environ Assess Manag; 2009 Apr; 5(2):248-58. PubMed ID: 19127981 [TBL] [Abstract][Full Text] [Related]
48. Understanding cross-data dynamics of individual and social/environmental factors through a public health lens: explainable machine learning approaches. Jeong S; Yun SB; Park SY; Mun S Front Public Health; 2023; 11():1257861. PubMed ID: 37954048 [TBL] [Abstract][Full Text] [Related]
49. Interpretable machine learning approach to analyze the effects of landscape and meteorological factors on mosquito occurrences in Seoul, South Korea. Lee DS; Lee DY; Park YS Environ Sci Pollut Res Int; 2023 Jan; 30(1):532-546. PubMed ID: 35900627 [TBL] [Abstract][Full Text] [Related]
50. Predicting Bulk Average Velocity with Rigid Vegetation in Open Channels Using Tree-Based Machine Learning: A Novel Approach Using Explainable Artificial Intelligence. Meddage DPP; Ekanayake IU; Herath S; Gobirahavan R; Muttil N; Rathnayake U Sensors (Basel); 2022 Jun; 22(12):. PubMed ID: 35746184 [TBL] [Abstract][Full Text] [Related]
51. Development of river ecosystem models for Flemish watercourses: case studies in the Zwalm river basin. Goethals P; Dedecker A; Raes N; Adriaenssens V; Gabriels W; De Pauw N Meded Rijksuniv Gent Fak Landbouwkd Toegep Biol Wet; 2001; 66(1):71-86. PubMed ID: 15952431 [TBL] [Abstract][Full Text] [Related]
52. Flow alteration-ecology relationships in Ozark Highland streams: Consequences for fish, crayfish and macroinvertebrate assemblages. Lynch DT; Leasure DR; Magoulick DD Sci Total Environ; 2019 Jul; 672():680-697. PubMed ID: 30974359 [TBL] [Abstract][Full Text] [Related]
53. An Interpretable Prediction Model for Identifying N Bi Y; Xiang D; Ge Z; Li F; Jia C; Song J Mol Ther Nucleic Acids; 2020 Dec; 22():362-372. PubMed ID: 33230441 [TBL] [Abstract][Full Text] [Related]
54. A novel coupling interpretable machine learning framework for water quality prediction and environmental effect understanding in different flow discharge regulations of hydro-projects. Nong X; Lai C; Chen L; Wei J Sci Total Environ; 2024 Nov; 950():175281. PubMed ID: 39117235 [TBL] [Abstract][Full Text] [Related]
55. Interpretation of Compound Activity Predictions from Complex Machine Learning Models Using Local Approximations and Shapley Values. Rodríguez-Pérez R; Bajorath J J Med Chem; 2020 Aug; 63(16):8761-8777. PubMed ID: 31512867 [TBL] [Abstract][Full Text] [Related]
56. Evaluating anthropogenic impacts on naturally stressed ecosystems: Revisiting river classifications and biomonitoring metrics along salinity gradients. Gutiérrez-Cánovas C; Arribas P; Naselli-Flores L; Bennas N; Finocchiaro M; Millán A; Velasco J Sci Total Environ; 2019 Mar; 658():912-921. PubMed ID: 30583186 [TBL] [Abstract][Full Text] [Related]
57. [Influence of species interaction on species distribution simulation and modeling methods.]. Wang YG; Zhang BR; Zhao R Ying Yong Sheng Tai Xue Bao; 2022 Mar; 33(3):837-843. PubMed ID: 35524539 [TBL] [Abstract][Full Text] [Related]
58. Developing an Explainable Machine Learning-Based Personalised Dementia Risk Prediction Model: A Transfer Learning Approach With Ensemble Learning Algorithms. Danso SO; Zeng Z; Muniz-Terrera G; Ritchie CW Front Big Data; 2021; 4():613047. PubMed ID: 34124650 [TBL] [Abstract][Full Text] [Related]
59. Modelling the effects of multiple stressors on respiration and microbial biomass in the hyporheic zone using decision trees. Mori N; Debeljak B; Škerjanec M; Simčič T; Kanduč T; Brancelj A Water Res; 2019 Feb; 149():9-20. PubMed ID: 30415026 [TBL] [Abstract][Full Text] [Related]
60. Assessing the ecological status of fluvial ecosystems employing a macroinvertebrate multi-taxon and multi-biomarker approach. Rodrigues C; Bio A; Guimarães L; Fernandes VC; Delerue-Matos C; Vieira N Environ Monit Assess; 2019 Jul; 191(8):503. PubMed ID: 31332534 [TBL] [Abstract][Full Text] [Related] [Previous] [Next] [New Search]